Drug Repurposing for Cancer Therapy: From Man to Molecules to Man

癌症治疗的药物再利用：从人到分子再到人

基本信息

批准号：
9337383
负责人：
Alexander Tropsha
金额：
$ 41.71万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9337383
关键词：
Address Adverse drug effect Animal Cancer Model Big Data Biological Biological Assay Biological Models Cancer Patient Cells Chemicals Clinical Trials Cognitive Collection Computer Simulation Data Data Set Databases Disease Docking Drug Modelings Drug Targeting Electronic Health Record Generations Goals Human In Vitro Internet Knowledge Discovery Label Laboratories Language Link Literature Malignant Childhood Neoplasm Malignant Neoplasms Medical Metadata Methods Mining Modeling Molecular Molecular Models Names Paper Pathway interactions Patients Pharmaceutical Preparations Phase II Clinical Trials Physicians PubChem PubMed Publications Publishing Quantitative Structure-Activity Relationship Reporting Research Research Project Grants Semantics Source Stream Structure Technology Text Therapeutic Effect Translations Validation base cancer therapy computer based Semantic Analysis computer framework computer studies computerized tools drug candidate genomic data health record high reward human data in vivo man new therapeutic target novel novel therapeutics pre-clinical repository screening small molecule libraries social media text searching tool virtual

项目摘要

Abstract This project aims to develop and implement multiple but integrated computational tools for drug repurposing by exploiting complimentary big data streams, i.e., unstructured texts (social media networks, published biomedical literature, and electronic health records), electronic databases of chemical-biological interactions and pathways, and laboratory data (biological screening of chemical libraries). We expect that tools to be developed in this project will be useful for repurposing observational textual data for research projects (addressing the second challenge of the underlying RFA). In addition, the envisioned translation of this data into a format amenable to quantitative modeling of drug effects will also enable integration of textual and laboratory data to create minable metadata (cf. the third challenge).

抽象的该项目旨在开发和实施用于药物的多种但集成的计算工具通过利用免费的大数据流，即非结构化文本（社交媒体）来重新利用网络，已发表的生物医学文献和电子健康记录），电子数据库化学生物相互作用和途径以及实验室数据（生物学筛查的生物学筛查化学库）。我们希望在该项目中开发工具对重新利用研究项目的观察性文本数据（解决第二个挑战基础RFA）。此外，将这些数据的设想翻译成格式适合对药物效应的定量建模也将使文本和实验室数据创建可最小的元数据（参见第三个挑战）。